Recently, New Delhi banned Uber in response to the alleged raping of a passenger by a driver. The UBER model is facing difficulties in India, primarily as a consequence of India’s poorly assembled/enforced justice infrastructure. A prerequisite to the sharing economy is trust. In the United States, it is possible for companies like UBER and AirBnB to succeed because trust exists as a consequence of a (somewhat) successfully constructed infrastructure. In the United States, the infrastructure is assembled such that any violation to a subscribed to code of conduct warrants consequences which can be enforced that deter an individual from committing the action due to the restrictions that may be imposed on the individual if the violation is committed (eg, jail time following a DUI).

This brings to mind a simple idea: the adoption of economic models which are based on sharing are likely correlated with a successfully constructed system of property rights and indicators of other social norms, such as corruption. Perhaps this assertion can be modeled and observed in the future.

Even in countries like the United States, companies like AirBnB and UBER will encounter a rocky road ahead, due to difficulties in adapting regulatory models to effectively address problems raised by these new models. I believe these companies will ultimately prevail. Digital connectivity has enabled individuals to derive value from assets that would otherwise be unused (eg, AirBnB’ing one’s apartment while one is on vacation); decreasing the waste from idleness associated from pure asset ownership and allowing a more effective allocation of general resources. Innovation always precedes regulation. Similar to the irrationality related to blaming Smith and Wesson for the tragic events which occurred at Columbine (and many other schools), the same applies to UBER. The derivative of this problem encompasses a greater scope than blaming new technologies; it is one of adapting innovation to the proper marriage between law, society, and technology within relevant contexts.